






'''simple docstring'''





import argparse

from torch import nn

# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
    ProphetNetForConditionalGeneration as ProphetNetForConditionalGenerationOld,
)
from transformers_old.modeling_xlm_prophetnet import (
    XLMProphetNetForConditionalGeneration as XLMProphetNetForConditionalGenerationOld,
)

from transformers import ProphetNetForConditionalGeneration, XLMProphetNetForConditionalGeneration, logging


_UpperCamelCase						:						Optional[Any]  =					logging.get_logger(__name__)
logging.set_verbosity_info()
def __UpperCAmelCase   (					A					:			str			,       A					:			str		)    ->   str:
			if "xprophetnet" in prophetnet_checkpoint_path:
						UpperCAmelCase_      :       int          =       XLMProphetNetForConditionalGenerationOld.from_pretrained(A		)
						UpperCAmelCase_    ,      UpperCAmelCase_      :       Optional[int]          =       XLMProphetNetForConditionalGeneration.from_pretrained(
						    A			,       output_loading_info=A		)
			else:
						UpperCAmelCase_      :       Union[str, Any]          =       ProphetNetForConditionalGenerationOld.from_pretrained(A		)
						UpperCAmelCase_    ,      UpperCAmelCase_      :       str          =       ProphetNetForConditionalGeneration.from_pretrained(
						    A			,       output_loading_info=A		)

			UpperCAmelCase_      :       str          =       ['''key_proj''', '''value_proj''', '''query_proj''']

			UpperCAmelCase_      :       Tuple          =       {
			    '''self_attn''': '''ngram_self_attn''',
			    '''cross_attn''': '''encoder_attn''',
			    '''cross_attn_layer_norm''': '''encoder_attn_layer_norm''',
			    '''feed_forward_layer_norm''': '''final_layer_norm''',
			    '''feed_forward''': '''''',
			    '''intermediate''': '''fc1''',
			    '''output''': '''fc2''',
			    '''key_proj''': '''k_proj''',
			    '''query_proj''': '''q_proj''',
			    '''value_proj''': '''v_proj''',
			    '''word_embeddings''': '''embed_tokens''',
			    '''embeddings_layer_norm''': '''emb_layer_norm''',
			    '''relative_pos_embeddings''': '''relative_linear''',
			    '''ngram_embeddings''': '''ngram_input_embed''',
			    '''position_embeddings''': '''embed_positions''',
			}

			for key in loading_info["missing_keys"]:
						UpperCAmelCase_      :       Any          =       key.split('''.'''		)

						if attributes[0] == "lm_head":
									UpperCAmelCase_      :       Dict          =       prophet
									UpperCAmelCase_      :       Any          =       prophet_old
						else:
									UpperCAmelCase_      :       Tuple          =       prophet.prophetnet
									UpperCAmelCase_      :       Optional[Any]          =       prophet_old.model

						UpperCAmelCase_      :       Any          =       False
						for attribute in attributes:
									if attribute in mapping:
												UpperCAmelCase_      :       List[str]          =       mapping[attribute]
												if not hasattr(A			,       A		) and len(A		) > 0:
															UpperCAmelCase_      :       List[Any]          =       attribute
									elif hasattr(A			,       A		):
												UpperCAmelCase_      :       Optional[int]          =       attribute

									if attribute == "weight":
												assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
												UpperCAmelCase_      :       int          =       old_model.weight
												logger.info(F"{attribute} is initialized."		)
												UpperCAmelCase_      :       List[str]          =       True
												break
									elif attribute == "bias":
												assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
												UpperCAmelCase_      :       Any          =       old_model.bias
												logger.info(F"{attribute} is initialized"		)
												UpperCAmelCase_      :       Tuple          =       True
												break
									elif attribute in special_keys and hasattr(A			,       '''in_proj_weight'''		):
												UpperCAmelCase_      :       Tuple          =       old_model.in_proj_weight.shape[0] // 3
												UpperCAmelCase_      :       Optional[Any]          =       getattr(A			,       A		)
												param.weight.shape == old_model.in_proj_weight[:embed_dim, :].shape, "Shapes have to match"
												param.bias.shape == old_model.in_proj_bias[:embed_dim].shape, "Shapes have to match"
												if attribute == "query_proj":
															UpperCAmelCase_      :       Tuple          =       nn.Parameter(old_model.in_proj_weight[:embed_dim, :]		)
															UpperCAmelCase_      :       Union[str, Any]          =       nn.Parameter(old_model.in_proj_bias[:embed_dim]		)

												elif attribute == "key_proj":
															UpperCAmelCase_      :       Optional[int]          =       nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]		)
															UpperCAmelCase_      :       int          =       nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]		)
												elif attribute == "value_proj":
															UpperCAmelCase_      :       Optional[int]          =       nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]		)
															UpperCAmelCase_      :       Tuple          =       nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]		)
												UpperCAmelCase_      :       List[str]          =       True
												break
									elif attribute == "position_embeddings":
												assert (
												    model.position_embeddings.weight.shape[-1] == old_model.embed_positions.weight.shape[-1]
												), "Hidden size has to match"
												assert model.position_embeddings.weight.shape[0] == 5_1_2, "We want 512 position_embeddings."
												UpperCAmelCase_      :       List[Any]          =       nn.Parameter(old_model.embed_positions.weight[:5_1_2, :]		)
												UpperCAmelCase_      :       Dict          =       True
												break

									if attribute.isdigit():
												UpperCAmelCase_      :       Any          =       model[int(A		)]
												UpperCAmelCase_      :       Optional[int]          =       old_model[int(A		)]
									else:
												UpperCAmelCase_      :       Any          =       getattr(A			,       A		)

												if old_attribute == "":
															UpperCAmelCase_      :       Optional[int]          =       old_model
												else:
															if not hasattr(A			,       A		):
																		raise ValueError(F"{old_model} does not have {old_attribute}"		)
															UpperCAmelCase_      :       Any          =       getattr(A			,       A		)

						if not is_key_init:
									raise ValueError(F"{key} was not correctly initialized!"		)

			print(F"Saving model to {pytorch_dump_folder_path}"		)
			prophet.save_pretrained(A		)


if __name__ == "__main__":
						_UpperCamelCase						:						str  =					argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '--prophetnet_checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.'
						)
						parser.add_argument(
						    '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
						)
						_UpperCamelCase						:						Optional[Any]  =					parser.parse_args()
						convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)










'''simple docstring'''





from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


_UpperCamelCase						:						int  =					logging.get_logger(__name__)
def __UpperCAmelCase   (					A					:			Union[tf.Tensor, np.ndarray]		)    ->   List[int]:
			if isinstance(A			,       np.ndarray		):
						return list(tensor.shape		)

			UpperCAmelCase_      :       str          =       tf.shape(A		)

			if tensor.shape == tf.TensorShape(A		):
						return dynamic

			UpperCAmelCase_      :       Any          =       tensor.shape.as_list()

			return [dynamic[i] if s is None else s for i, s in enumerate(A		)]
def __UpperCAmelCase   (					A					:			tf.Tensor			,       A					:			Optional[int] = None			,       A					:			Optional[str] = None		)    ->   tf.Tensor:
			return tf.nn.softmax(logits=logits + 1e-9			,       axis=A			,       name=A		)
def __UpperCAmelCase   (					A					:			List[str]			,       A					:			Union[str, Any]			,       A					:			List[str]			,       A					:			str=1e-5			,       A					:			Dict=-1		)    ->   List[Any]:
			# This is a very simplified functional layernorm, designed to duplicate
			# the functionality of PyTorch nn.functional.layer_norm when this is needed to port
			# models in Transformers.

			if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(A			,       A		):
						raise NotImplementedError('''Only 1D weight and bias tensors are supported for now, with only a single axis.'''		)

			# Get mean and variance on the axis to be normalized
			UpperCAmelCase_    ,      UpperCAmelCase_      :       int          =       tf.nn.moments(A			,       axes=[axis]			,       keepdims=A		)

			if axis != -1:
						# Reshape scale and weight to have the same rank as inputs, but with 1 dimensions
						# on every dimension except axis
						UpperCAmelCase_      :       Tuple          =       [1] * inputs.shape.rank
						UpperCAmelCase_      :       int          =       shape_list(A		)[axis]
						UpperCAmelCase_      :       Dict          =       tf.reshape(A			,       A		)
						UpperCAmelCase_      :       Optional[Any]          =       tf.reshape(A			,       A		)

			# Compute layer normalization using the batch_normalization
			# function.
			UpperCAmelCase_      :       Dict          =       tf.nn.batch_normalization(
			    A			,       A			,       A			,       offset=A			,       scale=A			,       variance_epsilon=A			,       )
			return outputs
def __UpperCAmelCase   (					A					:			Dict			,       A					:			int=0			,       A					:			Optional[int]=-1		)    ->   str:
			# Replicates the behavior of torch.flatten in TF

			# If end_dim or start_dim is negative, count them from the end
			if end_dim < 0:
						end_dim += input.shape.rank
			if start_dim < 0:
						start_dim += input.shape.rank

			if start_dim == end_dim:
						return input

			UpperCAmelCase_      :       Any          =       tf.shape(A		)
			UpperCAmelCase_      :       Dict          =       tf.math.reduce_prod(in_shape[start_dim : end_dim + 1]		)
			UpperCAmelCase_      :       Optional[int]          =       tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]]			,       axis=0		)
			return tf.reshape(A			,       A		)
def __UpperCAmelCase   (					A					:			tf.Tensor		)    ->   tf.Tensor:
			if not isinstance(A			,       tf.Tensor		):
						UpperCAmelCase_      :       Optional[Any]          =       tf.convert_to_tensor(A		)  # Catches stray NumPy inputs
			if encoder_attention_mask.shape.rank == 3:
						UpperCAmelCase_      :       Any          =       encoder_attention_mask[:, None, :, :]
			if encoder_attention_mask.shape.rank == 2:
						UpperCAmelCase_      :       Tuple          =       encoder_attention_mask[:, None, None, :]
			# T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
			# Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow
			# /transformer/transformer_layers.py#L270
			# encoder_extended_attention_mask = (encoder_extended_attention_mask ==
			# encoder_extended_attention_mask.transpose(-1, -2))
			UpperCAmelCase_      :       int          =       (
			    tf.cast(1			,       encoder_attention_mask.dtype		) - encoder_extended_attention_mask
			) * encoder_extended_attention_mask.dtype.min

			return encoder_extended_attention_mask
def __UpperCAmelCase   (					A					:			tf.Tensor			,       A					:			int			,       A					:			str = "input_ids"		)    ->   None:
			tf.debugging.assert_less(
			    A			,       tf.cast(A			,       dtype=tensor.dtype		)			,       message=(
			        F"The maximum value of {tensor_name} ({tf.math.reduce_max(A		)}) must be smaller than the embedding "
			        F"layer's input dimension ({embed_dim}). The likely cause is some problem at tokenization time."
			    )			,       )
def __UpperCAmelCase   (					A					:			Dict			,       A					:			Tuple			,       A					:			List[str]		)    ->   Union[str, Any]:
			UpperCAmelCase_      :       List[Any]          =       6_4_5_1_2
			# Check that no item in `data` is larger than `HDF5_OBJECT_HEADER_LIMIT`
			# because in that case even chunking the array would not make the saving
			# possible.
			UpperCAmelCase_      :       Tuple          =       [x for x in data if len(A		) > HDF5_OBJECT_HEADER_LIMIT]

			# Expecting this to never be true.
			if bad_attributes:
						raise RuntimeError(
						    '''The following attributes cannot be saved to HDF5 file because '''
						    F"they are larger than {HDF5_OBJECT_HEADER_LIMIT} "
						    F"bytes: {bad_attributes}"		)

			UpperCAmelCase_      :       Union[str, Any]          =       np.asarray(A		)

			UpperCAmelCase_      :       Optional[int]          =       1
			UpperCAmelCase_      :       str          =       np.array_split(A			,       A		)

			# This will never loop forever thanks to the test above.
			while any(x.nbytes > HDF5_OBJECT_HEADER_LIMIT for x in chunked_data		):
						num_chunks += 1
						UpperCAmelCase_      :       Dict          =       np.array_split(A			,       A		)

			if num_chunks > 1:
						for chunk_id, chunk_data in enumerate(A		):
									UpperCAmelCase_      :       int          =       chunk_data
			else:
						UpperCAmelCase_      :       List[Any]          =       data
def __UpperCAmelCase   (					A					:			int			,       A					:			Optional[Any]		)    ->   Tuple:
			if name in group.attrs:
						UpperCAmelCase_      :       Optional[int]          =       [n.decode('''utf8'''		) if hasattr(A			,       '''decode'''		) else n for n in group.attrs[name]]
			else:
						UpperCAmelCase_      :       Optional[int]          =       []
						UpperCAmelCase_      :       Optional[int]          =       0
						while "%s%d" % (name, chunk_id) in group.attrs:
									data.extend(
									    [n.decode('''utf8'''		) if hasattr(A			,       '''decode'''		) else n for n in group.attrs['''%s%d''' % (name, chunk_id)]]		)
									chunk_id += 1
			return data







def __UpperCAmelCase   (					A					:			Tuple		)    ->   str:

			def _expand_single_ad_tensor(A					:			int		):
						if isinstance(A			,       tf.Tensor		) and t.shape.rank == 1:
									return tf.expand_dims(A			,       axis=-1		)
						return t

			return tf.nest.map_structure(_expand_single_ad_tensor			,       A		)


